{"id":"W2075236172","doi":"10.1088/1367-2630/17/2/022005","title":"Quantum bootstrapping via compressed quantum Hamiltonian learning","year":2015,"lang":"en","type":"article","venue":"New Journal of Physics","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Perimeter Institute; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Industry Canada; Canada Excellence Research Chairs, Government of Canada","keywords":"Physics; Qubit; Ising model; Hamiltonian (control theory); Quantum; Bayesian inference; Bootstrapping (finance); Statistical physics; Quantum simulator; Bayesian probability; Algorithm; Computer science; Quantum computer; Quantum mechanics; Artificial intelligence; Mathematical optimization; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005008245,0.0002140539,0.0003777431,0.0001065371,0.0001581274,0.0002196932,0.001062121,0.00006139068,0.000001963598],"category_scores_gemma":[0.00006290329,0.0001828048,0.0001961051,0.0004231308,0.00004630672,0.0005734045,0.00019787,0.0008151254,0.00001684329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004800446,"about_ca_system_score_gemma":0.000389795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003807732,"about_ca_topic_score_gemma":8.833815e-7,"domain_scores_codex":[0.9981133,0.0001507935,0.0004785235,0.0002280802,0.000650896,0.0003784237],"domain_scores_gemma":[0.9982627,0.000124076,0.0005797503,0.0003086423,0.0002960605,0.0004287439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004788857,0.0002463475,0.0006005192,0.00003545521,0.000157252,0.0002588516,0.007007276,0.5940242,0.00950652,0.03808327,0.005561553,0.3444709],"study_design_scores_gemma":[0.000968754,0.000511774,0.000456287,0.0001259134,0.00001472463,0.0002548309,0.0000730987,0.9403656,0.001457555,0.04386385,0.01166247,0.000245107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07646485,0.0003814773,0.9208158,0.0008599436,0.001116563,0.0000475754,3.272716e-7,0.00008424377,0.000229239],"genre_scores_gemma":[0.9681352,0.00001284036,0.03013633,0.0001748256,0.001467071,2.419708e-7,8.061839e-7,0.00002097109,0.00005170715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8916703,"threshold_uncertainty_score":0.7454564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02836341012144184,"score_gpt":0.2561967476539574,"score_spread":0.2278333375325156,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}